Development of methods of intelligent management of security parameters of information systems

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.355570

Keywords:

multidimensionality of assessment, complex systems, efficiency of decision-making, efficiency of assessment, bio-inspired algorithms

Abstract

The object of the study is information systems (IS). The problem that is being solved in the study is an increase in the level of IS protection. The study developed a methodology for intelligent management of IS security parameters. The originality of the study consists of:

– conducting a multi-level and systematic assessment of the state of IS security using the proposed set of analytical expressions;

– determining the influence of IS security parameters on each other when the IS security state changes due to the use of fuzzy analytical expressions;

– construction of multidimensional dependencies of the security state of the special-purpose IS, which evaluates the security of the IS based on an arbitrary number of parameters;

– assessment of IS security in conditions of incompleteness of information about evaluation parameters, which solves the dimensionality problem;

– construction of time dependences of changes in parameters that characterize the state of IS protection, which allows determining the moments of deviation of their values from the nominal;

– reducing the error of assessing the state of IS security due to the human factor through the verification of IS parameters;

– attracting additional computing resources (if necessary), which achieves the prevention of looping of the methodology;

– determination of the influence of control decisions on a separately defined parameter for assessing the state of IS security, which achieves an increase in the accuracy of control influences.

Modeling of the work of the proposed methodology was carried out, during which it was established that increasing the security of the IS is achieved by increasing the efficiency of decision-making at the level of 12−16% due to the use of additional procedures and ensuring the reliability (correctness) of the decisions made at the level of 0.94. This allows to avoid distortions and distortions of the information provided for decision-makers (systems)

Author Biographies

Hennadii Shapovalov, Military Institute of Taras Shevchenko National University of Kyiv

Doctor of Philosophy (PhD), Senior Researcher

Research Department of Information Confrontation

Research Center

Olha Salnikova, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

Doctor of Sciences in Public Administration, Senior Researcher, Honored Worker of Science and Technology of Ukraine, Professor

Department of Theory and Practice of Management

Andrii Shyshatskyi, Kharkiv National Automobile and Highway University

Doctor of Technical Sciences, Senior Researcher, Professor

Department of Computer Science and Information Systems

Oleksii Kuvshynov, National Defence University of Ukraine

Doctor of Technical Sciences, Professor, Deputy Head of Institute

Institute of Professional Military Education «Leadership Training»

Oleksii Nalapko, Central Scientifically-Research Institute of Armaments and Military Equipment of the Armed Forces of Ukraine

Doctor of Philosophy (PhD), Doctoral Student

Scientific and Organizational Department

Oksana Dmytriieva, Kharkiv National Automobile and Highway University

Doctor of Economic Sciences, Professor, Head of Department

Department of Economics and Entrepreneurship

Hryhorii Stepanov, National Defence University of Ukraine

PhD, Associate Professor, Deputy Head of  Department

Department of Air Force

Yevhenii Kapran, Kruty Heroes Military Institute of Telecommunications and Information Technology

Adjunct

Scientific and Organizational Department

Viktor Yerko, State Research Institute of Aviation

PhD, Senior Researcher, Head of Department

Scientific Research Department

Ihor Borysov, Research Institute of Military Intelligence

PhD, Associate Professor, Deputy Head of the Institute for Scientific Work

References

  1. Sova, O., Radzivilov, H., Shyshatskyi, A., Shvets, P., Tkachenko, V., Nevhad, S. et al. (2022). Development of a method to improve the reliability of assessing the condition of the monitoring object in special-purpose information systems. Eastern-European Journal of Enterprise Technologies, 2 (3 (116)), 6–14. https://doi.org/10.15587/1729-4061.2022.254122
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. https://doi.org/10.21303/2461-4262.2020.001353
  5. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. https://doi.org/10.15587/1729-4061.2020.208554
  6. Wang, J., Neil, M., Fenton, N. (2020). A Bayesian network approach for cybersecurity risk assessment implementing and extending the FAIR model. Computers & Security, 89, 101659. https://doi.org/10.1016/j.cose.2019.101659
  7. Matheu-García, S. N., Hernández-Ramos, J. L., Skarmeta, A. F., Baldini, G. (2019). Risk-based automated assessment and testing for the cybersecurity certification and labelling of IoT devices. Computer Standards & Interfaces, 62, 64–83. https://doi.org/10.1016/j.csi.2018.08.003
  8. Henriques de Gusmão, A. P., Mendonça Silva, M., Poleto, T., Camara e Silva, L., Cabral Seixas Costa, A. P. (2018). Cybersecurity risk analysis model using fault tree analysis and fuzzy decision theory. International Journal of Information Management, 43, 248–260. https://doi.org/10.1016/j.ijinfomgt.2018.08.008
  9. Folorunso, O., Mustapha, O. A. (2015). A fuzzy expert system to Trust-Based Access Control in crowdsourcing environments. Applied Computing and Informatics, 11 (2), 116–129. https://doi.org/10.1016/j.aci.2014.07.001
  10. Mohammad, A. (2020). Development of the concept of electronic government construction in the conditions of synergetic threats. Technology Audit and Production Reserves, 3 (2 (53)), 42–46. https://doi.org/10.15587/2706-5448.2020.207066
  11. Bodin, L. D., Gordon, L. A., Loeb, M. P., Wang, A. (2018). Cybersecurity insurance and risk-sharing. Journal of Accounting and Public Policy, 37 (6), 527–544. https://doi.org/10.1016/j.jaccpubpol.2018.10.004
  12. Cormier, A., Ng, C. (2020). Integrating cybersecurity in hazard and risk analyses. Journal of Loss Prevention in the Process Industries, 64, 104044. https://doi.org/10.1016/j.jlp.2020.104044
  13. Hoffmann, R., Napiórkowski, J., Protasowicki, T., Stanik, J. (2020). Risk based approach in scope of cybersecurity threats and requirements. Procedia Manufacturing, 44, 655–662. https://doi.org/10.1016/j.promfg.2020.02.243
  14. Perrine, K. A., Levin, M. W., Yahia, C. N., Duell, M., Boyles, S. D. (2019). Implications of traffic signal cybersecurity on potential deliberate traffic disruptions. Transportation Research Part A: Policy and Practice, 120, 58–70. https://doi.org/10.1016/j.tra.2018.12.009
  15. Isong, A., Stephen, B. U.-A., Asuquo, P., Ihemereze, C., Enang, I. (2026). Machine learning based cloud computing intrusion detection. Advanced Information Systems, 10 (1), 115–125. https://doi.org/10.20998/2522-9052.2026.1.13
  16. Zarreh, A., Saygin, C., Wan, H., Lee, Y., Bracho, A. (2018). A game theory based cybersecurity assessment model for advanced manufacturing systems. Procedia Manufacturing, 26, 1255–1264. https://doi.org/10.1016/j.promfg.2018.07.162
  17. Zhuravskyi, Y. (Ed.) (2026). Intelligent decision support systems: methods for optimizing and supporting management decisions. Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-23-8
  18. Koval, M., Sova, O., Shyshatskyi, A., Artabaiev, Y., Garashchuk, N., Yivzhenko, Y. et al. (2022). Improving the method for increasing the efficiency of decision-making based on bio-inspired algorithms. Eastern-European Journal of Enterprise Technologies, 6 (4 (120)), 6–13. https://doi.org/10.15587/1729-4061.2022.268621
  19. Shyshatskyi, A. (Ed.) (2024). Information and control systems: modelling and optimizations. Kharkiv: TECHNOLOGY CENTER PC. https://doi.org/10.15587/978-617-8360-04-7
  20. Voznytsia, A., Sharonova, N., Babenko, V., Ostapchuk, V., Neronov, S., Feoktystov, S. et al. (2025). Development of methods for intelligent assessment of parameters in decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (4 (136)), 73–82. https://doi.org/10.15587/1729-4061.2025.337528
Development of methods of intelligent management of security parameters of information systems

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Published

2026-04-30

How to Cite

Shapovalov, H., Salnikova, O., Shyshatskyi, A., Kuvshynov, O., Nalapko, O., Dmytriieva, O., Stepanov, H., Kapran, Y., Yerko, V., & Borysov, I. (2026). Development of methods of intelligent management of security parameters of information systems. Eastern-European Journal of Enterprise Technologies, 2(4 (140), 16–25. https://doi.org/10.15587/1729-4061.2026.355570

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Section

Mathematics and Cybernetics - applied aspects